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All Journal Jurnal Informatika dan Teknik Elektro Terapan Information System for Educators and Professionals : Journal of Information System BERNAS: Jurnal Pengabdian Kepada Masyarakat Jurnal Bangkit Indonesia INFORMATION SYSTEM FOR EDUCATORS AND PROFESSIONALS : Journal of Information System Ramatekno Journal of Information Technology and its Utilization
Ricak Agus Setiawan
Politeknik Enjinering Indorama
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Journal : Journal of Information Technology and its Utilization

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Bahasa Inggris Heti Mulyani; Ricak Agus Setiawan; Musawarman; Annisa Romadloni
Journal of Information Technology and Its Utilization Vol 5 No 2 (2022): December 2022
Publisher : Sekolah Tinggi Multi Media

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56873/jitu.5.2.4894

Abstract

The spread of the coronavirus in Indonesia is quite fast. The spread of Covid 19 is almost evenly distributed in all provinces in Indonesia. Some areas even have a fairly high mortality rate. Therefore, it is necessary to group regions to find out which areas have the highest to lowest Covid cases so that the appropriate response process can be carried out. In addition, data visualization is also needed that provides information on COVID-19 data for each province. In this study, the data were grouped using the K-Means Clustering method. The dataset used is the Indonesian Covid-19 dataset from Kaggle. The criteria for each province's covid cluster are the number of cases and deaths. The Clustering process uses the Python programming language. From the results of this study, it can be seen that there are 3 groups of covid. The first group consists of 30 provinces with several cases below 200,000 and a number of deaths below 6000. The second group contains two provinces that have the highest number of cases, namely above 600,000, but the number of deaths is less than group 3, which is 15000. In group 3 there are 2 provinces where the number of cases is below 500,000 but the death rate is above 30,000.
Optimization of K Value in Clustering Using Silhouette Score (Case Study: Mall Customers Data) Heti Mulyani; Ricak Agus Setiawan; Halimil Fathi
Journal of Information Technology and Its Utilization Vol 6 No 2 (2023): December 2023
Publisher : Sekolah Tinggi Multi Media

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56873/jitu.6.2.5243

Abstract

Clustering is an important phase in data mining. The grouping method commonly used in data mining concepts is using K-Means. Choosing the best value of k in the k-means algorithm can be difficult. In this study the technique used to determine the value of k is the silhouette score. Then, to evaluate the k-means model uses the Davies Bouldin Index (DBI) technique. The best DBI value is close to 0. The parameters used are total consumer income and spending. Based on the results of this study it can be concluded that the silhouette score method can provide a k value with optimal results. For mall customer data of 200 data, the most optimal silhouette score is obtained at K = 5 with a DBI = 0.57.
CLUSTERING THE HAPPINESS LEVEL OF PROVINCES IN INDONESIA USING K-MEANS Heti Mulyani; Ricak Agus Setiawan
Journal of Information Technology and Its Utilization Vol 7 No 2 (2024): December 2024
Publisher : Sekolah Tinggi Multi Media

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56873/jitu.7.2.5854

Abstract

Community welfare is a government goal related to the fulfillment of basic needs, education and employment, which can be measured through the happiness index. The purpose of this research is to cluster provinces in Indonesia based on their resident’s happiness level. The data obtained from the Indonesian Central Bureau of Statistics website. The method used in this research is K-means clustering. There are 2 dimensions used, namely the personal dimension which includes education, employment, household income, health, housing conditions and, assets. The social dimension includes social relations, environmental conditions, security conditions, family harmony, and availability of free time. Based on the results of the study, 2 provincial groups were obtained based on the level of happiness. Testing is done using the Davies Bouldin Index (DBI). The optimal K is obtained, namely K = 2 with a DBI value of = 0.776. The first group is the happiest group including the provinces of North Maluku, Maluku, North Sulawesi, North Kalimantan, Gorontalo, Central Sulawesi, West Papua, Riau Islands, East Kalimantan. The other provinces are in the second group. The unhappiest groups are Banten, Bengkulu and Papua.
Co-Authors Ade Winarni Ahmad, Ilman Fadhilah Alfina Amelia Fitri Sanusi Amin Budi Utomo Annisa Romadloni Anugerah, Rafly Ardiansyah, Fabian Darnis, Rahmi Dwi Nurul Huda Eka Sartika, Eka Fadli Nurhidayat Fathi, Halimil Halimil Fathi Halimil Fathi Halimil Fathi Halimil Fathi Fathi Halimil, Halimil Ilivia Inayah Salsabila Inayah Salsabila Jamaludin Yasykurniaam Jamaludin Yasykurniaam Lufiah Sakinah Lutfiyah Sakinah Sakinah Muhammad Nugraha Muhammad Nugraha Muhammad Rafli Kamal Musawarman, Mr. Muswarman Muswarman Ni Nyoman Emang Smrti Niel Hilwert Simbolon Nopi Rahmawati Rahmawati Nursita Setiawati Nursita Setyawati Rahayu, Widya Andayani Rahmawati, Nopi Rahmi Darnis Raksa, Rizki Abiyu Rifialdi Faturrochman Roeswelly, Fadhly Aulia Sakinah, Lufiah Setyawati, Nursita Shelvy Sofiana Titania Pricillia Tono Widya Andayani Rahayau Wijaya, Muhammad Raffi Yuniar, Dara